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Positioning in Practice

Strategic Role of Marketing

For large firms that have two or more strategic business units (SBUs), there are generally three levels of strategy: corporate-level strategy, strategic-business-unit-level (or business-level) strategy, and marketing strategy.  A corporate strategy provides direction on the company=s mission, the kinds of businesses it should be in, and its growth policies.  A business-level strategy addresses the way a strategic business unit will compete within its industry.  Finally, a marketing strategy provides a plan for pursuing the company=s objectives within a specific market segment.  Note that the higher level of strategy provides both the objectives and guidelines for the lower level of strategy.

At corporate level, management must coordinate the activities of multiple strategic business units.  Thus the decisions about the organization=s scope and appropriate resource deployments/allocation across its various divisions or businesses are the primary focus of corporate strategy.  Attempts to develop and maintain distinctive competencies tend to focus on generating superior financial, capital, and human resources; designing effective organizational structures and processes; and seeking synergy among the firm=s various businesses.

At business-level strategy, managers focus on how the SBU will compete within its industry.  A major issue addressed in business strategy is how to achieve and sustain a competitive advantage.  Synergy for the unit is sought across product-markets and across functional department within the unit.

The primary purpose of a marketing strategy is to effectively allocate and coordinate marketing resources and activities to accomplish the firm=s objectives within a specific product-market.  The decisions about the scope of a marketing strategy involve specifying the target market segment(s) to pursue and the breadth of the product line to offered.  At this level of strategy, firms seek competitive advantage and synergy through a well-integrated program of marketing mix elements tailored to the needs and wants of customers in the target segment(s).

Strategic Role of Positioning

Based on the above discussion, it is clear that marketing strategy consists of two parts: target market strategy and marketing mix strategy.  Target market strategy consists of three processes: market segmentation, targeting (or target market selection), and positioning.  Marketing mix strategy refers to the process of creating a unique blend of product, distribution, promotion, and pricing strategies (the four P=s) designed to satisfying the needs and wants of customers.  Target market strategy and marketing mix strategy are closely linked and have a strong interdependence. The position of a product identified from the target market strategy serves as a guideline for formulating marketing mix strategy.

Market segmentation is the process by which a market is divided into distinct customer subsets of people with similar needs and characteristics that lead them to respond in similar ways to a particular product offerings and strategic marketing programs.  Targeting or target market selection is the process of selecting a segment or segments to serve by evaluating the relative attractiveness of each segment, the benefit sought, and the firm=s relative business strengths.  Finally, positioning is the process of designing product offerings and developing strategic marketing programs which collectively create an enduring competitive advantage in the target market.

The concept of target market strategy especially positioning is well-known and widely accepted by most marketing practitioners especially consumer goods managers as useful Atheoretical@ concepts in formulating marketing mix strategy.  In practice, however, marketers tend to bypass formal positioning and go directly to formulate marketing mix strategy.  This may be due to the fact that these managers do not know how to obtain perceptual maps, which are maps that show the positions of products on a set of primary customer needs.

The objective of this paper is to demonstrate a practical way for marketing practitioners to obtain perceptual maps for positioning and marketing mix strategy formulation.  Specifically, perceptual mapping and its relation to positioning are first discussed.  This is followed by discussion of statistical techniques that can be used to create perceptual maps.  Finally, a example of positioning process by factor analysis is demonstrated.

Perceptual Mapping: Identification of Strategic Benefits

Positioning is the perceived fit between a particular product and the needs of the target market, and thus positioning concept must be defined relative to the customer’s needs and competitive offerings.  It is one of the most important strategic concepts because it is concerned with differentiation.  Positioning reflect the careful efforts of marketing firms to portray the benefits they offer customers and to differentiate themselves from competition.  Positioning is critical for a product=s success.  Not only must the product deliver the benefits the customer needs, but it must do so better than competition.

Effective positioning requires assessing the positions occupied by competing products, determining the important dimensions underlying these positions, and choosing a position in the market where the organization=s marketing efforts will have the greatest impact.  An essential tool for strategic benefit positioning is perceptual maps.

Customer Needs and Perceptual Mapping: Method and Procedures

Perceptual maps represent the positions of products on a set of primary customer needs.  Perceptual maps visually summarized the dimensions that customers use to perceive and judge products and identify how competitive products are placed on those dimensions.  In practice, marketers need to know the number of dimensions, the names of those dimensions, what more detailed customer needs make up the dimensions, where competition is positioned, and where the ideal position for a new product or for repositioning is.

A set of useful consumer behavior model has been developed to handle consumer attitudes toward various brands in a marketplace.  Hauser and Urban (1977), in a new-product setting, describe the processing of product attributes as compression into smaller number of aggregate dimensions called Aevaluation criteria.@  The central idea is that the brands in a market can be represented as a set of points in a multidimensional space.  The axes of this space represent the perceived attributes that characterize the stimuli.  Two main analytical approaches most frequently used to derive evaluation criteria and build perceptual maps are decompositional methods, based on  multidimensional scaling, and compositional methods, based on factor analysis (Lilien and Kotler 1983).  Each of these procedures is discussed in the following section.

Multidimensional Scaling (MDS)

Multidimensional scaling (MDS) is a set of procedures in which a reduced space of product alternatives reflects perceived similarities and dissimilarities between products by the inter-product distances.  Exhibit 1 gives an example of the input and output of an MDS study of four graduate schools of business.  The idea behind it is to have the distances in part [b] have the same rank order as the direct similarity judgments in part [a].

To use multidimensional scaling to create perceptual maps:

1.                  Have customers evaluate existing products according to their relative similarity and form an average proximity matrix whose entries represent the similarities or dissimilarities among the products for each group of customers you wish to analyze.

2.                  Use multidimensional scaling to produce a map in 2, 3, ... dimensions.

3.                  Based on managerial judgments, limitations owing to the number of stimuli, and a plot of Astress@ select the appropriate number of dimensions.

4.                  Name the dimensions based on the relative position of the stimuli or a regression of the map coordinates on attribute ratings.

Multidimensional scaling is a powerful technique, but it must be used with caution.  Several issues need to be considered.  The first issue is concerned with the number of stimuli (i.e., products) needed.  Klabir (1969) shows that at least eight products are needed to create a good two-dimensional map.  Green and Wind (1973) suggest that the number of dimensions should be less than one-third of the number of products.  The second issue is concerned with the naming of the dimensions.  The analyst generally names the dimensions by using knowledge of the product category to explain best the products= positions.  This procedure is arbitrary and involves a high degree of creativity.  The final issue is concerned with the number of dimensions.  There is little theory to guide the selection of the number of dimensions.  However, the stress measure obtained from MDS can be plotted against the number of dimensions to determine when marginal changes in stress are becoming small.

Widely used, user-friendly statistical packages such as SAS and SPSS contain the programs for multidimensional scaling.  For example, in SPSS, one can obtain a multidimensional scaling analysis  from the statistics menu by choosing scale and then multidimensional scaling.

Factor Analysis

Factor analysis was originally developed in connection with efforts to identify the major factors making up human intelligence.  Educational and psychological researchers did not believe that every test in an educational battery measured a different facet of intelligence.  In fact, test scores for certain pairs of tests were highly intercorrelated, indicating that a more basic mental ability underlies test performance.  Factor analysis was developed to explain these intercorrelations in the test results of a few basic intelligence factors, subsequently identified as verbal ability, quantitative ability, and spatial ability.  Since that time, factor analysis has been applied to many other problems and is a frequently used technique in performing product-evaluation analyses in marketing.

The basic factor-analysis model assumes that original perceptual ratings about a product are generated by a small number of latent variables, or factors, and that the variance observed in each original perceptual variable is accounted for partly by a set of common factors and partly by a factor specific to that variable.  In the construction of a perceptual map by factor analysis, the positions of the products/brands studied can be obtained by averaging the factor scores of the respondents for each product/brand.  Factor scores are calculated from the matrix of factor-score coefficients, which describes factor scores as a linear function of the original ratings.

To use factor analysis to create perceptual map:

1.                  Have consumers rate all the products/brands under studied, one at a time, on a set of product attributes.  You can use Likert scales (scales anchored with strongly agree and strongly disagree) or semantic differential scales (scales with bipolar adjectives) in your questionnaire.

2.                  Analyze the data by factor analysis with rotation (e.g., with varimax rotation).  Also request for factor scores for all the products/brands.

3.                  Average the factor scores over all the respondents for each product/brand.

4.                  Use the average factor scores for each product/brand as coordinates to plot the position on the perceptual space.  Normally, two-dimensional maps are meaningful and easy to understand.  If more than two factors are extracted/identified from the set of product attributes, more than one two-dimensional maps may be generated.

5.                  Use factor loading table, which is an output representing the correlations between the attribute scales and the factors that the computer algorithm identified, to name the factors.

6.                  The ideal line (representing the relative importance of the factor scores in determining attitude toward the brand) can be identified from the multiple regression function with attitude as the dependent variable and factor scores as the independent variables.